Spectral clustering and model reduction for weakly-connected coherent network systems

Hancheng Min; Enrique Mallada

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arXiv · 2209.13701

Spectral clustering and model reduction for weakly-connected coherent network systems

Hancheng Min·Enrique Mallada

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Abstract

We propose a novel model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. Our approach is theoretically justified under a random graph setting. Finally, numerical experiments align with and validate our theoretical findings.

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Hancheng Min, Enrique Mallada. 2022-09-27. Spectral clustering and model reduction for weakly-connected coherent network systems. https://arxiv.org/abs/2209.13701

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